#107.1
crianca<-c(39,30,32,34,35,36,36,30)
adulto<-c(71,63,63,67,68,68,70,64)
alturas<-data.frame(crianca,adulto)
alturas.lm<-lm(adulto~crianca,data=alturas)
anova(alturas.lm)
alturas.conf<-confint(alturas.lm)
#discussao em sala
plot(alturas)
abline(alturas.lm, col="blue")
adulto.esp <- c(60,65,70,72,62,80,59,64)
crianca.esp <- adulto.esp/2
alturas.esp <- data.frame(adulto.esp,crianca.esp)
alturas.esp.lm <- lm(adulto.esp~crianca.esp)
coef(alturas.esp.lm)
plot(alturas.esp)
abline(alturas.esp.lm, col="red")

#107.2
library(MASS)
anim.m2 <- lm(log(brain)~log(body),data=Animals,subset=!(log(Animals$body)>8&log(Animals$brain)<6))
anim.m0 <- lm(log(brain)~1, data=Animals,subset=!(log(Animals$body)>8&log(Animals$brain)<6))
anova(anim.m0,anim.m2)
anova(anim.m2)
#1
anova(anim.m2)
#Obtm o mesmo resultado, uma vez que anim.m0  modelo nulo
#2
summary(anim.m0)
mean(log(Animals$brain[!(log(Animals$body)>8&log(Animals$brain)<6)]))
sd(log(Animals$brain[!(log(Animals$body)>8&log(Animals$brain)<6)]))
anim.m0 <- update(anim.m2, .~. -log(body))
#A mdia corresponde  estimativa do intercepto, enquanto o desvio padro, ao erro residual padro.

#107.3
head(iris)
setosa<-iris[iris$Species=="setosa",]
setosa$Species=="setosa"
head(setosa)
lm.iris<-lm(Sepal.Width~Sepal.Length,data=setosa)
lm.iris.coef<-coef(lm.iris)
larg.sep.com.pet<-lm(Sepal.Width~Petal.Length,data=setosa)
comp.sep.com.pet<-lm(Sepal.Length~Petal.Length,data=setosa)
res.lar<-residuals(larg.sep.com.pet)
res.comp<-residuals(comp.sep.com.pet)
lm.iris.nopetal<-lm(res.lar~res.comp)
lm.iris.nopetal.coef<-coef(lm.iris.nopetal)

#107.4
pressure
p<-pressure[,2]
t<-pressure[,1]
#plot(p~t)
#no
reg1<-lm(p~t)
reg2 <- update(reg1, .~. + I(t^2))
reg2.lm <- lm(p~t + I(t^2))
reg3 <- update(reg2,.~.+I(t^3))
reg3.lm <- lm(p~t + I(t^2) + I(t^3))
summary(reg1)
summary(reg2)
summary.reg3<-summary(reg3)
str(summary.reg3)
r2 <- summary.reg3$r.squared

#107.5
#107.5
aves<-read.csv("aves_cerrado.csv",header=TRUE,sep=";")
head(aves)
aves$fisionomia[aves$fisionomia=="ce"] <- "Ce"
aves$urubu[is.na(aves$urubu)] <- 0
aves$carcara[is.na(aves$carcara)] <- 0
aves$seriema[is.na(aves$seriema)] <- 0
CL <- aves[aves$fisionomia=="CL",]
CC <- aves[aves$fisionomia=="CC",]
Ce <- aves[aves$fisionomia=="Ce",]
mod.cl<-lm(seriema~carcara,data=CL)
mod.cc<-lm(seriema~carcara,data=CC)
mod.ce<-lm(seriema~carcara,data=Ce)
coef.cl<-coef(mod.cl)
coef.cc<-coef(mod.cc)
coef.ce<-coef(mod.ce)
s.cl<-summary(mod.cl)
p.cl<-s.cl$coefficients[2,4]
s.cc<-summary(mod.cc)
p.cc<-s.cc$coefficients[2,4]
s.ce<-summary(mod.ce)
p.ce<-s.ce$coefficients[2,4]


